Individuals tend to avoid cognitive demand, yet, individual differences appear to exist. Recent evidence from two studies suggests that individuals high in the personality traits Self-Control and Need for Cognition that are related to the broader construct Cognitive Effort Investment are less prone to avoid cognitive demand and show less effort discounting. These findings suggest that cost-benefit models of decision-making that integrate the costs due to effort should consider individual differences in the willingness to exert mental effort. However, to date, there are almost no replication attempts of the above findings. For the present conceptual replication, we concentrated on the avoidance of cognitive demand and used a longitudinal design and latent state-trait modeling. This approach enabled us to separate the trait-specific variance in our measures of Cognitive Effort Investment and Demand Avoidance that is due to stable, individual differences from the variance that is due to the measurement occasion, the methods used, and measurement error. Doing so allowed us to test the assumption that self-reported Cognitive Effort Investment is related to behavioral Demand Avoidance more directly by relating their trait-like features to each other. In a sample of N = 217 participants, we observed both self-reported Cognitive Effort Investment and behavioral Demand Avoidance to exhibit considerable portions of trait variance. However, these trait variances were not significantly related to each other. Thus, our results call into question previous findings of a relationship between self-reported effort investment and demand avoidance. We suggest that novel paradigms are needed to emulate real-world effortful situations and enable better mapping between self-reported measures and behavioral markers of the willingness to exert cognitive effort.
Selecting goals and successfully pursuing them in an uncertain and dynamic environment is an important aspect of human behaviour. In order to decide which goal to pursue at what point in time, one has to evaluate the consequences of one's actions over future time steps by forward planning. However, when the goal is still temporally distant, detailed forward planning can be prohibitively costly. One way to select actions at minimal computational costs is to use heuristics. It is an open question how humans mix heuristics with forward planning to balance computational costs with goal reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward planning, we used a novel stochastic sequential two-goal task. Comparing participants' decisions with an optimal full planning agent, we found that at the early stages of goal-reaching sequences, in which both goals are temporally distant and planning complexity is high, on average 42% (SD = 19%) of participants' choices deviated from the agent's optimal choices. Only towards the end of the sequence, participant's behaviour converged to near optimal performance. Subsequent model-based analyses showed that participants used heuristic preferences when the goal was temporally distant and switched to forward planning when the goal was close.
27Selecting goals and successfully pursuing them in an uncertain and dynamic environment is an 28 important aspect of human behaviour. In order to decide which goal to pursue at what point in time, 29 one has to evaluate the consequences of one's actions over future time steps by forward planning. 30 However, when the goal is still temporally distant, detailed forward planning can be prohibitively 31 costly. One way to select actions at minimal computational costs is to use heuristics. It is an open 32 question how humans mix heuristics with forward planning to balance computational costs with goal 33 reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward 34 planning, we used a novel stochastic sequential two-goal task. Comparing participants' decisions 35 with an optimal full planning agent, we found that at the early stages of goal-reaching sequences, in 36 which both goals are temporally distant and planning complexity is high, on average 42% (SD = 37 19%) of participants' choices deviated from the agent's optimal choices. Only towards the end of the 38 sequence, participant's behaviour converged to near optimal performance. Subsequent model-based 39 analyses showed that participants used heuristic preferences when the goal was temporally distant 40 and switched to forward planning when the goal was close. 41Author summary 42 When we pursue our goals, there is often a moment when we recognize that we did not make the 43 progress that we hoped for. What should we do now? Persevere to achieve the original goal, or 44 switch to another goal? Two features of real-world goal pursuit make these decisions particularly 45 complex. First, goals can lie far into an unpredictable future and second, there are many potential 46 goals to pursue. When potential goals are temporally distant, human decision makers cannot use an 47 exhaustive planning strategy, rendering simpler rules of thumb more appropriate. An important 48 question is how humans adjust the rule of thumb approach once they get closer to the goal. We 49 addressed this question using a novel sequential two-goal task and analysed the choice data using a 50 computational model which arbitrates between a rule of thumb and accurate planning. We found that 51 participants' decision making progressively improved as the goal came closer and that this 52 improvement was most likely caused by participants starting to plan ahead. 53 Introduction 54 Decisions of which goal to pursue at what point in time are central to everyday life [1-3]. Typically, 55in our dynamic environment, the outcomes of our decisions are stochastic and one cannot predict 56 with certainty whether a preferred goal can be reached. Often, our environment also presents 57 alternative goals that may be less preferred but can be reached with a higher probability than the 58 preferred goal. For example, when working towards a specific dream position in a career, it may turn 59 out after some time that the position is unlikely to be obtained, while another le...
Most research on risk aversion in behavioral science with human subjects has focused on a component of risk aversion that does not adapt itself to context. More recently, studies have explored risk aversion adaptation to changing circumstances in sequential decision-making tasks. It is an open question whether one can identify evidence, at the single subject level, for such risk aversion adaptation. We conducted a behavioral experiment on human subjects, using a sequential decision making task. We developed a model-based approach for estimating the adaptation of risk-taking behavior with single-trial resolution by modeling a subject's goals and internal representation of task contingencies. Using this model-based approach, we estimated the subject-specific adaptation of risk aversion depending on the current task context. We found striking inter-subject variations in the adaptation of risk-taking behavior. We show that these differences can be explained by differences in subjects' internal representations of task contingencies and goals. We discuss that the proposed approach can be adapted to a wide range of experimental paradigms and be used to analyze behavioral measures other than risk aversion.
When estimating ego-motion in environments (e.g., tunnels, streets) with varying depth, human subjects confuse ego-acceleration with environment narrowing and ego-deceleration with environment widening. Festl, Recktenwald, Yuan, and Mallot (2012) demonstrated that in nonstereoscopic viewing conditions, this happens despite the fact that retinal measurements of acceleration rate-a variable related to tau-dot-should allow veridical perception. Here we address the question of whether additional depth cues (specifically binocular stereo, object occlusion, or constant average object size) help break the confusion between narrowing and acceleration. Using a forced-choice paradigm, the confusion is shown to persist even if unambiguous stereo information is provided. The confusion can also be demonstrated in an adjustment task in which subjects were asked to keep a constant speed in a tunnel with varying diameter: Subjects increased speed in widening sections and decreased speed in narrowing sections even though stereoscopic depth information was provided. If object-based depth information (stereo, occlusion, constant average object size) is added, the confusion between narrowing and acceleration still remains but may be slightly reduced. All experiments are consistent with a simple matched filter algorithm for ego-motion detection, neglecting both parallactic and stereoscopic depth information, but leave open the possibility of cue combination at a later stage.
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